The theoretical and experimental objections to the Occam thesis do not
appear to have greatly diminished the machine learning community's
use of Occam's razor. This paper seeks to
support objections to the Occam thesis with robust and
general experimental counter-evidence. To this end it
presents a systematic procedure for increasing the complexity of
inferred decision trees without modifying their performance on the
training data. This procedure takes the form of a post-processor for
decision trees produced by C4.5
[Quinlan, 1993]. The application of this procedure to a range of
learning tasks from the UCI repository of learning tasks
[Murphy and Aha, 1993] is demonstrated to result, on average, in increased
predictive accuracy when the inferred decision trees are applied to
previously unseen data.